Array receiver with subarray selection

ABSTRACT

An array antenna system comprising an array of antenna elements and a receiver which uses a subset of the signals from the antenna elements, the selection of the subset of signals which should be used for a particular user is made on the basis of measurements of potential performance of the receiver with each subset of signals, combined, rather than of each individual signal.

TECHNICAL FIELD

The invention relates to a receiver system comprising an antenna and areceiver, the antenna comprising an array of antenna elements, and tothe receiver per se for use therein. The invention is especially, butnot exclusively, applicable to array receivers for use in base stationsof digital cellular telecommunications networks.

BACKGROUND ART

Mathematical expressions in this patent specification are based uponcomplex equivalent baseband notation. Essentially, complex quantitiesare used to represent the amplitude and phase of radio signals with theeffect of the carrier removed. Hence, if s₁(t) is the complex basebandequivalent of bandpass modulated signal s(t) and f_(c) is the carrierfrequency, we have:s(t)=Re[s ₁(t)e ^(j2πf) ^(c) ^(t)],  (1)where Re[·] denotes the real part of its argument and j=√{square rootover (−)}1.

Array antenna radio receivers typically are employed at the basestations of digital cellular communication systems (e.g. mobiletelephone networks, broadband wireless access for Internet and/orwide-area networking, etc.) to improve reception link quality (i.e.provide robustness against multipath fading) and/or reduce interferencelevels where interference can include thermal noise and man-made signalswhich exist in the desired signal's band. Since such systems typicallyaccommodate large numbers of simultaneously active users in any givencell or cell sector, the base station receiver must be capable ofmaintaining a plurality of radio links.

Known antenna array radio receiver systems comprise an array of antennaelements coupled to a signal receiving apparatus (also referred to as aradio-frequency (RF) front-end) which in turn is coupled to a signalprocessing apparatus. The signal receiving apparatus processes thesignals from the different antenna elements independently, in separatebranches, and performs on each signal standard downconversion,demodulation, filtering to isolate the channel of interest and,possibly, some transformation on the signal to bring it to a form usableby the signal processing apparatus (e.g. analog-to-digital conversion ifthe signal processor is digital). The signal processor takes theinformation from all of the branches (i.e. the demodulated, filtered andsuitably transformed signal data from each individual antenna element)and, using one of a number of appropriate known techniques, combines andprocesses it to extract a useful signal y(t), which is the best possibleestimate of the desired user signal.

In the context of wireless communications, the received vector x(t)(i.e. the received signal across all array elements) is made up of adesired signal s₀(t) transmitted by a wireless terminal, interferingsignals s₁(t) transmitted by competing terminals which operate in thesame frequency band or in adjacent bands with some amount of crosstalkbeing present, and white noise. Hence $\begin{matrix}{{x(t)} = {{{c_{0}(t)}{s_{0}(t)}} + {\sum\limits_{i = 1}^{M}\quad{{c_{i}(t)}{s_{i}(t)}}} + {n(t)}}} & (2)\end{matrix}$where c₁(t) is an N×1 vector of complex elements describing the channelsfrom the ith terminal to all of the N array elements, M is the number ofinterfering signals and n(t) is the white noise vector.

In such a context, the function of the antenna array radio receiver isto isolate the desired signal s₀(t) from the interferers and white noiseas well as compensate for distortions introduced in the channel c₀(t)(e.g. multipath fading) so that, at all times, the array output y(t)approximates the desired signal s₀(t) as closely as possible.

Typically, the combination of the signals from the individual elementsis simply a linear weight-and-sum operation. If an N-element array isconsidered and x(t) is the N×1 vector of the array element outputs, thearray output is defined asy(f)=w(t)^(H) x(t),  (3)where w(t) is the N×1 complex weight vector and (·)^(H) denotes thehermitian transpose (i.e. complex conjugate transpose) of its argument,be it a vector (as it is in the above) or a matrix. Although it istime-varying, the weight vector varies slowly compared to the input andoutput signals. When a combiner operates according to equation (3), itis termed a linear combiner and the entire receiver is designated alinear array receiver.

Given an N-element linear array, it is theoretically possible to null upto N−1 interferers although at the cost of some degree of noiseenhancement. However, arrays can also be employed to provide a diversitygain against multipath fading (since deep fades will rarely occur onmore than one branch at a time provided that the antenna elements arespaced sufficiently apart). It is known that a K+M-element array cannull up to M−1 interferers while providing a diversity improvement oforder K+1 against multipath fading. It is also known that an optimumcombiner (described below) implicitly allocates degrees-of-freedom(DOFs) to interference rejection first. Leftover DOFs, if any, areemployed to combat fading.

Typically, the receiver collects statistics of the input signal and usesthem to derive a weight vector which minimizes some error measurebetween the array output y(t) and the desired signal s₀(t). The mostcommon error measurement is the mean-square errorε=<[y(t)−s ₀(t)]² >=<[w ^(H)(t)x(t)−s ₀(t)]²>,  (4)which forms an N-dimensional quadratic surface with respect to theweight vector elements. It thus has a single global minimum. Theminimization of this criterion forms the basis of mean-square-error(MSE) minimizing linear array receivers or, equivalently, minimummean-square-error (MMSE) linear array receivers (also called optimumcombiners). (In the following equation (5), and others to follow, thedependence upon t is omitted for the sake of clarity.) Adaptivefiltering theory indicates that the best combination of weights for agiven sequence of received data is given byw=R _(xx) ⁻¹ c ₀,  (5)where R_(xx) is the covariance matrix of the received array outputs andis given byR _(xx) =<x(t)x ^(H)(t)>,  (6)where (·) denotes the expectation (i.e., the ensemble average) of itsargument.

Such array receivers are suitable for use where time dispersion due tomultipath propagation does not extend significantly beyond a singlesymbol period. That is, there is little or no intersymbol interference(ISI).

When the channels carrying useful signals do exhibit significant ISI,the traditional solution is to use an equalizer, which is an adaptivefilter whose purpose is to invert the channel impulse response (thusuntangling the ISI) so that the overall impulse response at its outputwill tend to have an ideal, flat (or equalized) frequency spectrum.

The signal processing portion of the standard linear equalizer works inthe same way as a linear adaptive array receiver except that the signalsources are not points in space (i.e., the array of antenna elements)but points in time. The signals are tapped at a series of points along asymbol-spaced delay line (termed a tapped delay line or TDL), thenweighted and combined.

While the implementation of the signal processing apparatus for bothequalizer and array receiver can be identical (minimization of the MSEby adaptive weighting of the inputs) the performance will differ.Because signals are physically sampled at different points in space bythe array receiver, it is very effective at nulling unwanted signalsources or co-channel interference (CCI). However, it has limitedability against intersymbol interference (ISI) due to dispersive, i.e.,frequency-selective, fading, since the latter is spread in time. On theother hand, the equalizer is adept at combatting ISI but has limitedability against CCI.

In environments where both ISI and CCI are present, array reception andequalization may be combined to form a space-time processor. The mostgeneral form of the latter is obtained when each weighting multiplier ina narrowband array is replaced by a full equalizer for a total of Nequalizers. Again, the implementation of the signal processing apparatuswill be identical and will rely on equation (3) supra. The onlydifference is that the weight vector w and the input vector x will belonger. Indeed, for an equalizer length of L taps and an array size of Nelements, the vectors w and x will both have LN elements.

The canonical linear mean-square-error minimizing space-time receiver(i.e. the most obvious and immediate linear space-time receiverstructure and also in certain respects the most complex) comprises anantenna array where each array element output is piped to a finiteimpulse response (FIR) adaptive filter, which in this context isreferred to as an equalizer. Each adaptive filter comprises atapped-delay line having taps spaced by a symbol period or a fraction ofa symbol period. For good performance, the length of the tapped-delayline should be equal or superior to the average channel memory length.In many cases, the number of taps this implies can be very large (e.g.10-100 per adaptive filter). An important special case is where thechannel memory length is of the order of a symbol period. The channel isthen said to be flat fading and the adaptive filters in each branch arereduced to a single weighting complex multiplier. This simplifiedstructure is termed a narrowband array or spatial processor.

On the other hand, if the channel memory length is more than a singlesymbol period, the channel is subjected to frequency-selective fading(also called time dispersive or simply dispersive fading) thus inducingintersymbol interference (ISI) at the receiver. Such a situationrequires the more general structure with a complete adaptive filter perbranch; such a system is variously designated as wideband array orspace-time processor.

The weights multiplying each tap output must be constantly adapted tofollow the changes in the characteristics of the desired user's andinterferers' channels. In a representative class of such systems, theweights are computed on a block-by-block basis (block adaptation) andeach block contains a training sequence of known training symbols forthat purpose. In digital wireless communications systems, the block usedfor adaptation purposes will typically correspond to a data packet asdefined by the networking protocol in use.

By adapting the weights to minimize a global performance index, i.e. themean-square error between the desired signal and the S-T receiveroutput, the receiver implicitly performs the following:

-   -   reduces or eliminates intersymbol interference (ISI) caused by        frequency-selective fading in wideband channels;    -   reduces or eliminates co-channel interference (CCI) from nearest        cells where carriers are reused or from inside the cell, since        the space-time processor permits reuse of carriers within the        cell or the sector thanks to its power of spatial        discrimination—often referred to as Space Division Multiple        Access (SDMA).    -   improves output SNR (due to the array's larger effective        aperture).

The number of temporal elements depends primarily upon the degree ofintersymbol interference and could be between say, 10 and 100. Thenumber of spatial elements depends upon the number of antenna elementsand could be, say, 10. The number of antenna elements is chosen as afunction of the maximum number of interferers to be nulled and thedesired gain against fading.

Since wireless systems are typically interference-limited (i.e.,interference is the main impediment which prevents capacityincrease—accommodating more active users—above a certain limit), thefirst two benefits of space-time processors are of most interest inorder to increase capacity To achieve maximal benefit, it is better tocombine the array with carrier reuse-within-cell (RWC), also calledspace-division multiple access (SDMA). In known such systems, separateS-T processors will have to be implemented for every user (allprocessors share the same physical antenna array and front-end receivercircuitry but have distinct equalizers and combiners). This can resultin a prohibitively complex receiver system from a numerical and/orhardware complexity standpoint, especially if the memory length L of thechannels is large and regardless of whether RWC is used or not.Therefore, it is of great relevance to develop reduced-complexityspace-time receiver architectures.

It is known to reduce complexity and/or hardware requirements of anarray receiver by using a single RF receiver and selecting differentantenna elements in turn. This is termed selection diversity and itprovides some gain against multipath fading but, in general, little orno gain against CCI.

It is also known to do so by selecting a subset of the signals from theantenna elements, for each user, and processing those.

In the context of wireless communications, when a remote stationtransmits a signal to the array antenna, multipath effects will resultin destructive/constructive interference, so the signals in each branch,i.e., extracted from the different antenna elements, will have differentsignal-to-noise ratios. Also, the signal may be strongest in a certainangular sector or cone, depending upon the configuration of the antennaarray. Indeed, little scattering occurs in the immediate vicinity of anelevated base station such that most received energy will typically beconcentrated in a narrow angle around a single main direction ofarrival.

It is known, therefore, to select and process only a subset of thesignals comprising those with the highest signal-to-noise ratio, asdisclosed, for example, in an article entitled “SNR of GeneralizedDiversity Selection Combining with Nonidentical Rayleigh FadingStatistics” by N. Kong and L. B. Milstein, IEEE Transactions onCommunications, Vol. 48, No. 8, pp. 1266-1271, August 2000. Adisadvantage of these techniques is that they base the subset selectionupon instantaneous measured power in each branch, which still entails asignificant amount of hardware complexity and/or computational overhead.Indeed, while only as many complete RF front-ends as subset elements maybe required, all array elements must be monitored at all times, possiblyusing a plurality of signal power measurement devices. Moreover, asoftware-radio-type implementation will require the processor to pollthe said measurement devices frequently thus introducing undesirableoverhead.

A further disadvantage of such known techniques is that they do notdifferentiate between interference from other users and white noise Itis possible that a subset of branch signals with the highest individualsignal-to-noise ratios, when combined, will not perform as well as adifferent subset in which one or more of the branch signals have lowerindividual signal-to-noise ratios. For example, the latter subset ofsignals might involve interferers whose signals tend to negate eachother so that, when combined, they produce a better overall signalquality.

U.S. Pat. No. 6,081,566 issued Jun. 27, 2000 by Molnar et al. disclosesa receiver which bases subset selection upon a number of criteriaincluding signal quality as measured from signal power and so-called“impairment power”. This is not entirely satisfactory, however, becausethe signal quality measurement still is computed for each individualbranch and so could still result in a sub-optimum subset being selected.

DISCLOSURE OF INVENTION

An object of the present invention is to at least ameliorate one or moreof the problems associated with the above-mentioned known array antennasystems. To this end, in embodiments of the present invention, theselection of the subset of signals which should be used for a particularuser is made on the basis of measurements of potential performance ofeach subset of signals, rather than of each individual signal.

In this specification, the term “user” will be used to denote a remotetransmitter whose signals are received by the receiver section.

According to one aspect of the present invention, an array receiversystem, for receiving signals from a plurality of transmitting users,comprises an array of antenna elements (22/1, . . . , 22/10) and areceiver having a plurality of receiver sections, each corresponding toa different one of the users, the receiver sections each having a signalprocessing unit (160) for processing and combining a subset of signalsfrom the antenna elements to produce a received signal for thecorresponding user, the receiver further comprising switching means(180) for selecting a plurality of different subsets of signals from theantenna elements for processing for the signal processing unit (160),each subset consisting of a predetermined number of said signals, eachsignal processing means serving to control the switching means to changethe signals comprising the subset of signals used by the correspondingreceiver section in dependence upon a measure of the potentialperformance of that receiver section with different subsets of saidplurality of signals, said measure being based upon the combined subsetof signals. (SDMA)

Where the array receiver system is to be used in a space-divisionmultiple access (SDMA) communications system, the switching means maycomprise a switch matrix in each receiver section, and the receivercomprise a plurality of radio frequency (RF) front-end sections eachcoupling a respective one of the antenna elements to each of saidswitching means and each of the signal processing means. Each front-endsection would convert the signal from the corresponding antenna elementto a format suitable for processing by said processing means, and eachof said switch matrices select subsets of the converted signals forapplication to the associated one of the different receiver sections.

Where the array receiver system is to be used in a non-SDMA system (i.e.where the receiver is concerned with a single desired user per carrier),each receiver section may comprise a plurality of radio frequency (RF)front-end units equal in number to the number of signals in each of saidsubsets coupled to the signal processing means, and the switching meansmay comprise a switch matrix for coupling selected ones of the antennaelements to respective ones of the RF front-end sections of eachreceiver section, each RF front-end section for converting the subset ofsignals from the corresponding antenna elements to a format suitable forprocessing by said processing means.

The measurement of the performance of the different subsets may becarried out periodically, preferably making use of samples of knowntraining sequences embedded in the received signal.

It is envisaged that the initial subset selection could be made when theremote station is establishing communications with the receiver, perhapsduring the usual identification/authentication procedure. Subsequentchanges to the selected subset may be performed using standardcontinuous (i.e. tracking) algorithms which do not require knowntraining sequences or pilot symbols,

The antenna array may comprise a radial array of directive elements,especially if intended for use at a base station.

In the context of a cellular telephony system, receivers embodying theinvention could be used at either a base station or a mobile station.When used in a mobile station, the receiver usually would have a singlereceiver section with as many RF front end sections as the subset size,thus reducing RF hardware requirements. This is advantageous because thenarrow beamwidth antenna element patterns—which may or may not overlapwith one another—constitute a form of pre-filtering given the fact thatany received signal (desired or interference) at an elevated basestation will normally have most of its energy concentrated within anarrow cone. This spatial prefiltering is helpful because it reduces thenumber of elements (i.e. subset size) required to obtain a given levelof performance.

Alternatively, the same prefiltering can be applied when, instead of aradial array of directive antenna elements, an array of omnidirectionalantenna elements is used, followed by a preprocessing beamformingmatrix. The said matrix provides as outputs linear combinations of thearray elements' outputs where the linear combinations are chosen toemulate the patterns of a radial array.

Preferably, the signal processing unit measures said performance bymonitoring statistics of the signals derived from the different subsetsover a time period long enough to average out fast fading effects due tophase relationships of multipath components of the subset signals.

In essence, what is captured in the long-term statistics is theinstantaneous value of the “shadowing” (i.e. slow fading) coefficientsas well as the correlation properties of the fast fading (as opposed toits instantaneous values).

This arrangement advantageously allows the subset selection process tobe performed relatively infrequently thus lowering the associatedcomputational burden without undue performance penalty.

Preferably, the statistics gathered for the purpose of subset selectioninclude an average (long-term) spatial (or space-time in a space-timeembodiment) covariance matrix characterizing the desired signal and asimilar covariance matrix characterizing the impairment lumpedinterference and thermal noise). Other statistics which could beemployed include:

-   -   (i) Instantaneous (i.e. short-term) covariance matrices        otherwise as described above;    -   (ii) Instantaneous desired signal power at all elements (and        time delays in a space-time embodiment);    -   (iii) Instantaneous signal-to-interference-plus-noise ratio        (SINR) at all elements (and time delays in a space-time        embodiment);    -   (iv) Instantaneous desired signal power and interference power        at all elements (and time delays in a space-time embodiment);    -   (v) Instantaneous desired signal power and short-term or        long-term interference covariance matrix.

Other aspects of the invention include the receiver per se and themethod of operating the array antenna receiver system.

According to another aspect of the invention, there is provided a methodof receiving signals from a plurality of transmitting users using anarray antenna having an array of antenna elements (22/1, . . . , 22/10)and a receiver having a plurality of receiver sections (12 ₀, . . . , 12₇), each corresponding to a different one of the users and coupled tothe antenna elements by a switching means (18 ₀), the method comprisingthe steps of;

periodically selecting different subsets of signals from the antennaelements, processing and combining each subset of signals anddetermining potential performance of the receiver section of aparticular user with that subset, determining which of the subsets wouldprovide best performance, and controlling the switching means to changethe signals comprising the subset of signals used by the correspondingreceiver section.

Embodiments of the invention do not seek to identify all degrees offreedom of the desired users channel, but rather exploits thedirectivity of the array elements to select the S most significantelements in order to achieve the minimum mean-square error. Such aselection is not really based on identifying the degrees-of-freedom, ormodes, of the desired users channel since interferers are also takeninto account in the selection process. It is a procedure tointelligently reduce (by exploiting the geometry of the impinging waves)the number of array degrees-of-freedom that require active adaptation inorder to achieve a proportional reduction in both numerical and hardwarecomplexity.

The size of subset S will be assumed fixed and the most useful choices(depending on the desired complexity/performance tradeoff) are likely tobe between 2 and 4 elements, inclusively. However, it should be pointedout that the essence of the invention does not depend on the size of thesubsets being fixed and it is easy to imagine an extension where thesubset size would be selected adaptively (e.g. signals with large anglespreads would be allocated larger subarrays).

For a fixed array subset size S, there are $\begin{matrix}{{N_{S} = \left( \frac{N}{S} \right)},} & (7)\end{matrix}$possible subsets [S₁, S₂, . . . ,S_(N) _(s) ]. The subset selectioncould theoretically be performed to minimize the mean-square error (or,equivalently, to maximize the signal-to-interference-plus-noise ratio(SINR)) according to: $\begin{matrix}{{S_{opt} = {\max\limits_{S_{s}}\left\langle {c_{0}^{{(S_{s})}^{H}}R_{I + N}^{{(S_{s})}^{- 1}}c_{0}^{(S_{s})}} \right\rangle}},{{{for}\quad s} = 1},\ldots\quad,N_{s},} & (8)\end{matrix}$where c₀ is the N×1 desired user signature (i.e. vector channel) acrossthe array, á×ñ denotes the medium-term average of its argument andR_(I+N) is the N×N short-term interference-plus-noise covariance matrixat the array input and can be expressed as a function of the interferingusers signatures: $\begin{matrix}{R_{l + N}^{(S_{s})} = {\sum\limits_{m = 1}^{M}\quad{c_{m}^{(S_{s})}c_{m}^{{(S_{s})}^{H}}}}} & (9)\end{matrix}$where M is the number of co-channel interferers.

In almost all terrestrial propagation environments, it is well-knownthat narrowband (i.e. flat fading) wireless channels can be accuratelyrepresented in the short-term as either zero mean (Rayleigh-type fading)or non-zero mean (Rician-type fading) complex gaussian variables. Itfollows that a signature vector c_(m) ^((S) ^(s) ⁾ taken at any timeinstant is a complex gaussian vector characterized by its medium-termcovariance matrix (and mean vector in the Rician case). In the rest ofthis document, Rayleigh fading will be assumed for the sake of clarityalthough the principles outlined and the invention itself apply just aswell to the Rician case.

The selection criterion in (8) can be averaged over the small-scalefading and then rewritten in terms of the medium-term covariancematrices as follows; $\begin{matrix}{{S_{opt} = {\max\limits_{S_{s}}\quad{{tr}\left\lbrack {\sum\limits_{0}^{(S_{x})}\quad\sum\limits_{l_{0}}^{{(S_{s})}^{- 1}}} \right\rbrack}}},\quad{{{for}\quad s} = 1},\ldots\quad,{N_{s}.}} & (10)\end{matrix}$where Σ₀ ^((S) ^(s) ⁾ is the medium-term-averaged covariance matrix ofthe desired user vector channel over array subset S_(s) and tr[·] standsfor the trace of its matrix argument. Likewise, Σ_(m) ^((S) ^(s) ⁾ isthe medium-term covariance matrix of the nth users vector channel oversubset S_(s) and${\Sigma_{l_{0}}^{(S_{s})} = {\sum\limits_{m = 1}^{M}\quad\Sigma_{m}^{(S_{s})}}}\quad$is the covariance matrix of the interference affecting user 0.

Basing the subset selection process on the medium-term statisticsimplies that subset selection can be performed at negligible numericalcost (e.g. as a background task) and may also reduce hardwarerequirements. Indeed, the medium-term covariance matrices can be assumedfixed for periods of the order of a second in mobile wireless systems[23] and even longer in fixed wireless systems (such as proposedbroadband wireless systems, e.g. the Local Multipoint DistributionService (LMDS)).

It should be noted that the system described here does not rely onmulti-user information (although some minor algorithmic reduction incomplexity is possible in a multi-user context) and can thus constitutea more natural upgrade path for existing systems where each users signalis typically processed independently. Also, the relative reduction ofcomplexity is approximately the same whether the system is implementedas a narrowband processor (in flat fading environments) or a widebandprocessor (in dispersive fading environments).

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described by way of exampleonly and with reference to the accompanying drawings, in which:

FIG. 1 is a simplified block schematic diagram of part of an arrayantenna radio receiver system, for a SDMA system, comprising a firstembodiment of the invention;

FIG. 2 is a flowchart depicting computation of estimates of covariancematrices in the receiver system of FIG. 1;

FIG. 3 is a flowchart depicting determination of subset selections inthe receiver system of FIG. 1;

FIG. 4 is a flowchart depicting computation of MMSE weight vectors inthe receiver system of FIG. 1;

FIG. 5 is a simplified block schematic diagram of a receiver system forSDMA, which is a second embodiment of the invention;

FIG. 6 is a flowchart depicting computation of covariance matrices in areceiver system which does not employ SDMA; and

FIG. 7 is a flowchart depicting determination of subset selection in thereceiver system of FIG. 5; and

FIG. 8 is a simplified block schematic diagram of a space-time receiverembodying the invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Referring to FIG. 1, an array antenna receiver system for receivingsignals from a plurality of user transmitters (not shown) comprises anantenna having a plurality of antenna elements, specifically 6 elements22/1, . . . , 22/10, coupled by way of a bank of radio frequency (RF)front end units 26/1, . . . , 26/10 to an array receiver which hasseveral receiver sections, one for each of the user transmitters. Eightreceiver sections (0,1, . . . 7) are illustrated, but there could bemore.

The RF “front-end” units 26/1, . . . , 26/10 are identical and ofconventional construction. Only one will be described, with reference tothe inset diagram in FIG. 5. As shown inset in FIG. 5, RF front-end unit26/1 comprises a RF/IF downconverter 28/1, a channel filter 30/1 (whichisolates only the required channel and rejects out-of-band noise andinterference), and an analog-to-digital converter unit 32/1 forperforming bandpass sampling. Alternatively, the IF or RF signal couldbe downconverted to baseband prior to A/D conversion. The output of theA/D converter unit 32/1 is also the output of RF front-end unit 26/1 andis coupled to each of the array receiver sections.

The receiver sections are identical so only that for user 0 is shown indetail and will be described with reference to FIG. 1 again.

Receiver section 0 comprises a selector unit, specifically a RF 6×3matrix switch 18 ₀, having six input ports 20/1 ₀, . . . , 20/10 ₀,connected to respective outputs of the six RF front-end units 26/1, . .. , 26/10, and three output ports connected to respective data inputs ofa signal processing unit 16. A control input of the matrix switch 18 ₀,is connected to a control signal output of the signal processing unit 16₀. The outputs of all six RF front-end units 26/1, . . . , 26/10 areconnected to the signal processing unit 16 ₀. The signal processing unit16 ₀ can be implemented as a custom Very Large Scale Integration (VLSI)chip, a Field Programmable Gate Array (FPGA) or as software running on aDigital Signal Processor (DSP).

The signal processing unit 16 ₀ performs signature (i.e. desired uservector channel) and covariance matrix estimation, MMSE processing,weighting and combining, matched filtering and detection of symbols. Thelatter two are standard digital radio receiver operations and so are notdepicted specifically in FIG. 1 and will not be described in detailherein.

Again, for simplicity of description, operation of only the signalprocessing unit 16 ₀ for the one desired user m is depicted in FIG. 1and will be described; it should be appreciated that similar signalprocessing units will be provided for the other users (transmitterstations) and will process the corresponding subset of signals.

The three outputs of the RF matrix switch 180 are shown connected withinthe signal processing unit 16 ₀ to multipliers 34/1 ₀, 34/2 ₀ and 34/3₀, respective outputs of which are coupled to a summing device 36 ₀whose output is coupled to later stages of the receiver, via a detector38 ₀, which is conventional and need not be described in detail here.

The multipliers 34/1 ₀, 34/2 ₀ and 34/3 ₀ multiply the digital signalsfrom the three RF front-end units 26/1, 26/2 and 26/3 by weights w₁(0),w₂(0) and w₃(0), respectively, supplied by a minimum mean square error(MMSE) computing unit 40 ₀ which functionally is implemented by thesignal processing unit 16 ₀. The MMSE weight computation unit 40 ₀updates the weights using MMSE criteria in known manner according toequation 5, supra.

The signal processor unit 16 ₀ also performs the subset selectionprocess and so is shown as including a short term channel estimator 42connected to the RF front end units, a long term channel estimator 44 ₀and a subset selector unit 46 ₀, conveniently a logic circuit. The shortterm channel estimator 42 ₀ extracts channel parameters using thesignals from the RF front-end units and supplies them to the MMSE weightcomputations means for use in updating the weights being used for aparticular subset of signals. The long term channel estimator 44 ₀monitors long term statistics and uses them to determine whether or notto control the matrix switch 18 ⁰ to select a different subset ofsignals for a particular user. The subset selector unit 46 ₀ could, ofcourse, be separate from the processor unit 16 ₀.

In operation, the signal processing unit 16 ₀ monitors the signals fromall six of the antenna elements 22/1, . . . , 22/10, conductsstatistical analysis upon each different subset of the prescribed numberof elements (three in this case) and periodically operates the matrixswitch 18 ₀ to select a different trio of the antenna elements 22/1, . .. , 22/10 if the current subset selection is producing inferiorperformance than would be expected using one of the other subsets, aswill be explained more fully later.

Operation of the array receiver shown in FIG. 1 will be described ingeneral terms for user 0 and on the basis that the subarray subset sizeS is fixed. It should be noted that, as is conventional, in thefollowing description, the desired user is deemed to be user 0 and theinterferers are deemed to be users 1 to M; hence there are M+1 users inthe system.

Moreover, without loss of generality, the description will assume thenarrowband case. Hence, each branch in a selected subset is multipliedby a single complex weight (as opposed to being filtered by a fullequalizer).

In operation, the long term channel estimator 44 ₀ of the signalprocessor unit 16 ₀ uses a “long term” loop, illustrated in FIGS. 2 and3, to compute subset selection for a particular user based uponmeasurements of the performance of the receiver with different subsetsof the antenna elements and the short term channel estimator 42 ₀ uses a“short term” loop, illustrated in FIG. 4, to compute and update weightsto optimize the performance with the selected subset.

Implementation using SDMA implies that the receiver must handlesimultaneously multiple users on the same carrier frequency.

Long-term Loop of SDMA Implementation

The long-term loop updates the estimates of the long-term covariancematrix. The covariance matrix embodies the statistical characteristicsof the time-varying channel for a particular user, user 0 in this case.Since each element of the receiving array “sees” a slightly differentchannel, the overall channel can be represented as a vector of Nelements and characterized by an N×N covariance matrix. In this case,the long term estimator 44 ₀ of signal processing unit 16 ₀ computes along-term covariance matrix, that is a matrix which has been measuredand averaged over a period which is long enough to eliminate the effectof the multipath fading (also called fast fading). As a result, enoughinformation is retained to identify the principal modes of the fadingprocess (which correspond to the larger eigenvalues of the covariancematrix) even without the instantaneous behavior of the fading processbeing known. The said modes change at a much slower rate than themultipath fading itself but do in themselves provide enough informationto preprocess the signals intelligently. Use of the long-term covariancematrix in selecting the optimal subsets thus makes subset selection along-term, inexpensive (in terms of processing power and/or hardwarecomplexity) process. The actual fading fluctuations are dealt withentirely within the optimal subsets by the short-term loop, as will bedescribed later.

The flowcharts shown in FIGS. 2 and 3 represent two distinct sections ofthe long-term loop: the portion illustrated in FIG. 2 is the long-termcovariance matrix estimation while FIG. 3 corresponds to determinationof the subset selection. Hence, the subset selection is based strictlyon long-term information and does not take into account theinstantaneous multipath fading. This is suboptimal, but the performancepenalty is deemed to be more than compensated by the reduction incomplexity thus achieved.

While the receiver comprises ten antenna array elements 22/1, . . . ,22/10 and ten RF front-end sections 26/1, . . . , 26/10, they are eachshared by a pool of receiver sections 12 ₀, . . . , 12 ₇, one for eachdesired user. The receiver sections 12 ₀, . . . , 12 ₇ have user'ssignal processing units 16 ₀, . . . , 16 ₇, respectively, each of whichmay be mapped to a different subset of antenna elements. The patterns ofthese array element subsets are in turn determined by the MMSE spatialfiltering performed by the short-term loop. Since each of these patternscan be effectively “steered” to favour a desired signal and nullinterferers, many users can coexist on the same carrier frequency.Hence, in this SDMA implementation, what the signal processing unitcorresponding to one user rejects as interference can be a desiredsignal to the signal processing unit corresponding to another user.

It is assumed without loss of generality that this is a packet-basedsystem. Each user is assigned a unique training sequence which isincorporated in the packet (e.g., as a prefix, a suffix, a “mid-amble”as in the GSM cellular telephony standard, or as a sequence distributedthroughout the packet). The training sequence is determined and assignedby whatever network protocol applies within the system, i.e., it couldbe fixed or it could be assigned upon entry into the network, or someother way of establishing agreement between the base station and thesubscriber station as to which training sequence should be used fortheir communications.

It is also assumed that the packets are of fixed length and that thislength is shorter than the coherence time of the channels in theintended band and environment of operation. This implies that a packetis short enough that the multipath fading channel can be consideredfixed over its duration.

Extensions of the implementation described herein to systems with longerand/or variable-length packets (e.g., longer than the coherence time ofthe channels), to CDMA systems (where the user's codes can be exploitedas continuously-present training sequences) and to non-packet systemswill be obvious to practioners of the art.

In this preferred embodiment, each packet contains the known trainingsequence of 32 bits and this is used by each of the receiver sections toidentify a given signal from the corresponding user and extract itschannel characteristics through correlation. The information thusgathered from each packet is used to update the long-term covariancematrices used in subset selection. It is also used immediately by theshort-term loop to adapt the weights of the combiner/spatial filter,thus determining the pattern of the array subset which will best enhancereception of the desired signal and reject the interferers.

Accordingly, as evidenced by the flowchart in FIG. 3, the receiver isconcerned with the received training sequences rather than the entirecontent.

With the goal of continuously updating the long-term covariancematrices, the receiver will sample the packets periodically, maybe everythird packet or so, extract the training sequence and then compute thechannel parameters using that particular training sequence. Thissampling rate defines what will be called the estimation interval. Ifthe packet arrival rate is variable, an appropriate strategy should bedevised (instead of picking every nth packet) so that the samplinginterval remains fairly constant in time.

Referring now to the flowchart in FIG. 2, steps 2.1, 2.2 and 2.3 merelycomprise a preamble to detect the beginning of the estimation intervaland next time slot and capture the training sequence; and areself-explanatory in step 2.4, the processor 14 computes the short-termcovariance matrix for user 0 ({circumflex over (R)}₀) To situate thisoperation properly in time, an index i is introduced so that {circumflexover (R)}₁ is the short-term covariance matrix estimate obtained duringthe ith estimation interval. Assuming that the training sequence 0 (S₀)for the user has a length of K symbols and the vector x[k,i] is theoverall received vector across the array corresponding to the kth symbolof the training sequence in the ith estimation interval; the covariancematrix estimate is obtained in step 2.4 by correlation with the trainingsequence as follows: $\begin{matrix}{{{\hat{R}}_{0}\lbrack i\rbrack} = {\left( {\sum\limits_{k = 1}^{K}\quad{x_{({k,i})}{s_{0}\lbrack k\rbrack}}} \right)\left( {\sum\limits_{k = 1}^{K}\quad{x_{({k,i})}^{H}{s_{0}^{*}\lbrack k\rbrack}}} \right)}} & (11)\end{matrix}$where s₀[k] is the kth symbol in user 0's sample training sequence.

Therefore, R₀[i] is the ith estimate of the short-term covariance matrixderived from a single packet for user 0. It is equal to the estimate ofuser 0's vector channel (obtained by correlation with the trainingsequence) multiplied by its transposed conjugate. Mathematically, thisis expressed {circumflex over (R)}₀[i]=ĉ₀ĉ₀ ^(H).

Hence, the vector channel estimate for any user m is obtained bycorrelation as $\begin{matrix}{{\hat{c}}_{m} = {\sum\limits_{k = 1}^{K}\quad{x_{({k,i})}{{s_{m}\lbrack k\rbrack}.}}}} & (12)\end{matrix}$

In step 2.5, the running estimate of user 0's long-term covariancematrix ({circumflex over (Σ)}₀) is updated according to $\begin{matrix}{{{{\hat{\Sigma}}_{0}\lbrack i\rbrack} = {{\gamma{{\hat{\Sigma}}_{0}\left\lbrack {i - 1} \right\rbrack}} + {\frac{1 - \gamma}{K}{{\hat{R}}_{0}\lbrack i\rbrack}}}},} & (13)\end{matrix}$where {circumflex over (Σ)}₀[i−1] is the estimate from the previousestimation interval and γ is the forgetting factor. This factor willtypically take values between 0.8 and 0.99 and determine at what ratenew information (embodied by {circumflex over (R)}₀[i]) will replace oldinformation obtained in previous estimation intervals. Its value ischosen according to how fast the channel parameters are changing and howoften the estimates are being taken. Generally, higher values of γ implythat information obtained in previous estimates has a longer life, i.e.,it is forgotten slowly.

There are similar steps to compute the covariance matrix estimates forevery user m (with m=0 . . . M). Thus, FIG. 2 shows steps 2.6 and 2.7which correspond to steps 2.4 and 2.5 and compute the covariance matrixestimates for user 1 and steps 2.8 and 2.9 which correspond to steps 2.4and 2.5 and compute the covariance matrix for user 7, the last user inthis example. Depending on low-level implementation details, thecovariance matrices can be computed for all users simultaneously (i.e.,if parallel processing is employed and/or if replicated signalprocessing hardware has been provided to that effect) or sequentially(as in a single processor firmware implementation or a single dedicatedsignal processing circuit is being reused).

Once covariance matrices have been computed for all users, these areused in turn to compute, for each user, an interference covariancematrix estimate, ie., a covariance matrix characterizing the sum of theinterfering signals seen by the user in question, that is all users butthe user in question. One possible method to compute {circumflex over(Σ)}₁ _(m) [i], the interference covariance matrix for user m, is bysumming the covariance matrices for all users but user m, i.e.$\begin{matrix}{{{\hat{\Sigma}}_{l_{m}}\lbrack i\rbrack} = {\sum\limits_{\frac{m = 0}{m \neq 0}}^{M}\quad{{{\hat{\Sigma}}_{\quad_{m}}\lbrack i\rbrack}.}}} & (14)\end{matrix}$Steps 2.10, 2.11 and 2.12 in FIG. 2 illustrate this for users 0, 1 and7.

FIG. 3 illustrates by a flowchart the process of selecting antennaelement subsets, which is also part of the long-term loop. The startingpoint of the flowchart in FIG. 3 is in fact the input of all thecovariance matrices and interference covariance matrices from FIG. 2.

Since there are 10 antenna elements and that the subsets each have asize of 3 elements, there are 120 possible such combinations ofelements. Consequently, the selection algorithm will cycle through everyone of those combinations and determine, for each subset, a performancecriterion (based on long-term channel information gathered in theprocess of FIG. 2) and select for each user the subset which yields themaximum value of that performance criterion. It should be noted thateach user will in general be assigned a different subset. This is why inthe SDMA implementation there is a RF front-end unit for every element,i.e. in the receiver of FIG. 1, there is one of the RF front-end units26/1, . . . , 26/10 for each of the elements 20/1, . . . , 20/10.

In a non-SDMA implementation, as will be describer later, there is asingle desired user and therefore a single subset of RF front-end unitsis active at all times. Therefore, only as many RF front-ends as thesubset size (3 in this example) are required and these can be assigneddynamically through RF switches to the array elements making up theselected subset.

Thus, step 3.1 sets subset index s to 1 and user index m to 0. In step3.2, the 10×10 element covariance matrix for user m will be used to form(by picking the appropriate rows and columns corresponding to theelements of the subset) a 3×3 covariance matrix or submatrix for user mand subset selection s=1. In step 3.3, the same thing is done to theinterference covariance matrix for user m to form a subset interferencecovariance matrix for user m. Step 3.4 determines whether or not thesubset index equals the maximum, in this case 20; if it does not, itincrements the subset index and repeats steps 3.2 and 3.3.

Once the subset covariance matrices and subset interference covariancematrices have been created for all possible subsets, step 3.6 determinesthe optimum subset S_(opt) ^((m)) for user m. This is done by computinga performance criterion for every possible subset and selecting 30 thesubset that yields the highest value of the said criterion. Thus;$\begin{matrix}{S_{opt}^{(m)} = {\max\limits_{S}\quad{{tr}\left\lbrack {\sum\limits_{m}^{\overset{\bigwedge}{(S_{g})}}\quad\left( \sum\limits_{l_{m}}^{\overset{\bigwedge}{(S_{g})}}\quad \right)^{- 1}} \right\rbrack}}} & (15)\end{matrix}$

The invention embraces the use in step 3.6 of a number of differentperformance criteria based on long-term information. In thisimplementation, however, the chosen criterion is essentially a measureof the best possible achievable SINR for a given subset on average(since it is based on long-term information).

In step 3.7, the optimum subset is transferred to the subset selectorfor user m and step 3.8 determines whether or not this process has beenperformed for all of the users. If it has not, step 3.9 increments theuser index and steps 3.2 to 3.8 are repeated.

Once the optimum subset has been computed for every desired user, step3.8 returns the algorithm to the very beginning, i.e., the long-termloop is repeated, starting with step 2.1 which waits for the nextestimation packet to arrive. It is presumed that every packet includes atraining sequence, but the long-term loop samples them periodically.

In FIG. 3, step 3.6 is shown in more detail in an inset diagram. Asshown in the inset diagram, step 3.6.1 again sets the subset index s toone and sets another index (S_(max)) representing the best or optimumsubset also to one.

Step 3.6.2 then sets a variable max equal to 0 and step 3.6.3 computes ameasure of SINR (the performance criterion) which we call C. Thiscriterion is computed as the trace of the covariance matrix estimate foruser m and subset S₁ times the inverse of the interference covariancematrix estimate for user m and subset selection S₁. This is expressed$\begin{matrix}{C = {{{tr}\left\lbrack {\sum\limits_{m}^{\overset{\bigwedge}{(S_{g})}}\quad\left( \sum\limits_{l_{m}}^{\overset{\bigwedge}{(S_{g})}}\quad \right)^{- 1}} \right\rbrack}.}} & (16)\end{matrix}$

In step 3.6.4, the criterion computed in 3.6.3 is compared with the marvariable which n step 3.6.2 was initially set to 0. If C>max then step3.6.6 lets max=C and S_(max)=s since the current subset is the bestsubset so far. In step 3.6.5, it is verified whether he last subset(s=N_(s)) has been reached. If not, s is incremented in 3.6.7 and steps3.6.3-3.6.5 are repeated. Once all subsets have been processed, S_(max)contains the index of the best subset for user m and therefore step3.6.8, lets S_(opt)^((m)) = S_(s_(max)).Short-term Loop for SDMA Implementation

Once the subset selections have been made for each of the users for thatparticular estimation interval, the next step is to optimize theperformance of each subset. This entails adjusting the weights that areused in processing the signals from the antenna elements in eachparticular subset, as will be described with reference to the flowchartshown in FIG. 4. The weights are updated continually in parallel with,and at a faster rate than subset selection. In fact, the short-term loopis performed once for every packet received. In FIG. 4, it is assumedthat packets for all M+1 users are received simultaneously and hencesteps 4.5-4.9 are repeated for every user.

Thus, step 4.1 waits for the next time slot to begin and then step 4.2stores the received signal, i.e., the vector for the entire array of 10elements, in a buffer for the interval corresponding to the trainingprefix. This implies that packets for all users are synchronized and alltraining sequences are received simultaneously. In systems where this isnot the case, appropriate adjustments can easily be made. It is theinterval corresponding to the reception of the training sequences whichis stored for further processing.

In step 4.3, an estimate is taken of the short-term overall covariancematrix R_(xx) over the entire array of elements. This is done accordingto $\begin{matrix}{{\hat{R}}_{xx} = {\sum\limits_{k = 1}^{K}\frac{{x\lbrack k\rbrack}{x^{H}\lbrack k\rbrack}}{K}}} & (17)\end{matrix}$

Hence, K symbols are captured by step 4.2 and these symbols areprocessed by computing the sum over k of the kth sample x[k] multipliedby its complex conjugate transpose X^(H)[k] and dividing the result byK.

Step 4.4 then sets the user index m to 0 and step 4.5 extracts from thematrix {circumflex over (R)}_(xx) a submatrix $\begin{matrix}{\hat{R}}_{yy}^{(S_{-})} & \quad\end{matrix}$which is the set of elements out of {circumflex over (R)}_(xx) whichcorresponds to the current chosen subset S_(m) for user m thus yieldinga 3×3 matrix.

Step 4.6 estimates user m's spatial signature across the subset S_(m)according to the expression $\begin{matrix}{{{\hat{c}}^{S_{m}} = {\frac{1}{K}{\sum\limits_{k = 1}^{K}{{y^{S_{m}}\lbrack k\rbrack}{s_{m}^{*}\lbrack k\rbrack}}}}},} & (18)\end{matrix}$where y^(S) ^(m) [k] is the received signal vector across subset S_(m)corresponding to the kth symbol in the training sequence and S*_(m)[k]is the complex conjugate of the kth symbol of the training sequence foruser m.

It should be noted that the equation above for step 4.6 is basicallyvery similar to the one in box 2.4 except that it is computed acrosssubset S_(m) instead of across the entire array.

In step 4.7, the spatial signature computed in step 4.6, i.e., ĉ_(m)^((S) ^(m) ⁾ (or the vector channel estimate across only the subset ofelements rather than the entire array) is used to compute the weightvector according tow={R _(yy) ^((S) ^(m) ⁾}⁻¹ ĉ _(m) ^((S) ^(m) ⁾.  (19)

This weight vector comprises a series of weights, one for each elementof the subset. Hence, in the specific embodiment, where there are threeelements in each subset, there would be three weights. These weights arethen transferred (step 4.8) to the MMSE processor for user m where theyare used to multiply the signals from each element of the subset priorto summation to derive the best estimate of the desired signal in theMMSE (Minimum Mean-Square Error) sense.

Step 4.9 then determines whether or not the user index m is set to M,i.e., the weights have been computed for all the desired users. If not,step 4.10 increments the user index m to m+1 and steps 4.5 to 4.9 arerepeated.

Once all the weight vectors have been computed, step 4.9 returns thealgorithm to step 4.1 to wait for the beginning of the next time slotwhereupon the weights will be computed again and updated.

For the purpose of comparing complexity, assume, for example, a 10 Mb/ssystem with packets of 68 bytes (roughly the size of an ATM cellincluding a training sequence). A guard byte is inserted between eachpair of successive packets. Consider a set of 8 users who send packetssimultaneously once every ten slots on the same carrier. Since there are18115.94 slots per second, the users of interest are transmitting at arate of 1811.59 packets per second. At this rate, channels typicallywill be sufficiently different from one packet to the next due tomultipath fading to warrant retraining at every packet. Furthermore,each packet contains a known training sequence of 32 bits. The long-termcovariance matrix is assumed to have a worst-case 90% correlation timeof 0.5 s; its estimate will be updated every 0.1 s and the subsetselection will also be performed every 0.1 s.

In the case of the radial array of 10 antenna elements with a subsetsize of 3, the relative computational load with respect to conventionalMMSE array processing is roughly 26%. With a subset size of 2, it isapproximately 20%.

In the case of a multi-user receiver, there is no advantage in havingthe RF switch immediately after the antenna elements since it is likelythat the collectivity of users, each using a different subset ofelements, will at some instants in time require all elements to beactive. In other words, the union of all subsets can at times includeall elements in the array and thus N RF front end units are required.Assuming all M co-channel interferers are in this case valid users,there are M+1 distinct signal processing units which also can bephysically distinct (in separate integrated circuits or DSP units) orcombined in a single multi-user unit or partitioned into any number ofphysical units in any way according to practical design considerations

It will be appreciated that the invention is not limited to SDMAreceiver systems. Application to a SDMA receiver system will now bedescribed by way of example with reference to mainly to FIGS. 5, 6 and7. It should be noted that the short-term loop illustrated in FIG. 4 isalmost the same for both SDMA and non-SDMA implementations. Also, in thelong-term loop of a non-SDMA implementation, the receiver deals withonly one desired user at a time per carrier. The receiver would bereplicated for other users existing at other carriers (as would be thecase in a SDMA embodiment too).

Referring to FIG. 5, in which components corresponding to those in thereceiver system of FIG. 1 have the same reference numerals, an arrayantenna receiver system for receiving signals from a plurality of usertransmitters in a non-Space Division Multiple Access (SDMA) system(e.g.wireless LAN, cellular telephone) comprises an antenna having aplurality of antenna elements 22/1, . . . , 22/6 coupled to an arrayreceiver 12 which comprises a radio frequency unit 14 and a signalprocessing unit 16. The antenna is connected to the receiver 12 by aselector unit 18 which is a radio frequency matrix switch having sixinput ports 20/1, . . . , 20/6 coupled to respective ones of a radialarray of antenna elements 22/1, . . . , 22/6 and three output ports24/1, 24/2 and 24/3 coupled within the radio frequency unit 12 to RFfront end units 26/1, 26/2 and 26/3, respectively.

The RF “front-end” units 26/1 m, 26/2 m and 26/3 m are identical and ofconventional construction. As shown inset in FIG. 5, RF front-end unit26/1 comprises a RF/IF downconverter 28/1, a channel filter 30/1 (whichisolates only the required channel and rejects out-of-band noise andinterference), and an analog-to-digital converter unit 32/1 forperforming bandpass sampling. Alternatively, the IF or RF signal couldbe downconverted to baseband prior to A/D conversion. The output of theA/D converter unit 32/1 (which is also the output of RF front-end unit26/1) is coupled to the signal processing unit 16 which can beimplemented as a custom Very Large Scale Integration (VLSI) chip, aField Programmable Gate Array (FPGA) or as software running on a DigitalSignal Processor (DSP).

The signal processing unit 16 is nearly identical to that described withreference to FIG. 1 and so will not be described again. As before, itperforms signature (i.e. desired user vector channel) and covariancematrix estimation, MMSE processing, weighting and combining, matchedfiltering and detection of symbols. Also, it performs the subsetselection process using a long-term channel estimator 44 which controlsthe matrix switch 18 and updates the MMSE weights for a particularsubset selection by means of short term channel estimator 42.

In this case, the signal processor 16 will operate the matrix switch 18periodically to take the receiver section “off line” temporarily whileit selects one of the other subsets and obtains the sample of thetraining sequence. This will be repeated for each of the other subsetsin turn to obtain the long-term statistics. Depending upon the system,it may be necessary to acquire the long-term statistics by selecting thesame subset several times during such “off line” intervals.

Long-term Loop for Non-SDMA Implementation

Just like in the SDMA implementation, the long-term loop updates theestimates of the long-term covariance matrix. In this case, however,there is only one desired user which is user 0. Indeed, it is assumedthat carrier frequencies are not reused within a single cell or sectorbut rather the array serves to improve link quality by combattinginterference on the same carrier from neighbouring cells or sectorspossibly reducing the carrier reuse distance.

The long-term is composed of two main sections: the portion illustratedin FIG. 6 is the long-term covariance matrix estimation while FIG. 7corresponds to subset selection.

In this non-SDMA implementation, because a single received signalprocessing unit is required, for user 0, it has exclusive usage of theantenna array and the RF front-end section. It follows that the numberof RF front-ends required is determined by the subset size (i.e., 3 inthe specific implementation) instead of the array (i.e., 10).

This implementation also assumes that the system is packet-based withpackets being shorter than the channel coherence time. Furthermore,adaptation (that is extraction of useful channel parameters) is based onthe presence of a training sequence as a preamble (or postamble ormidamble or distributed sequence) to the main packet body. The systemassumptions are generally similar to the SDMA implementation except forthe following points:

-   -   Since there is only one desired user, the interferers' packets        need not be synchronized with the desired user's packets. In        fact, the structure of the interferers' signals is entirely        irrelevant and they need not be packet-based at all.    -   Since no covariance matrix estimate is needed for the individual        interferers, a different method than the one described in the        SDMA implementation is called for to compute the interference        covariance matrix.

Referring to FIG. 6, steps 6.1, 6.2 and 6.3 are identical to steps 2.1,2.2 and 2.3 of the SDMA implementation. Likewise, steps 6.4, 6.5 computethe running estimate {circumflex over (Σ)}₀[n] in a manner identical tosteps 2.4 and 2.5. Steps 6.6, 6.7 and 6.8 introduce a new method ofcalculating the interference covariance matrix (which could also beemployed in an alternative SDMA implementation). In step 6.6, theoverall short-term covariance matrix is computed according to$\begin{matrix}{{{\hat{R}}_{xx}\lbrack i\rbrack} = {\sum\limits_{k = 1}^{K}{x_{\lbrack{k,l}\rbrack}{x_{\lbrack{k.l}\rbrack}^{H}.}}}} & (20)\end{matrix}$

In step 6.7, {circumflex over (R)}_(xx)[i] is used to update a runningestimate of the long-term overall covariance matrix {circumflex over(Σ)}_(xx)[i] according to $\begin{matrix}{{\underset{xx}{\hat{\sum}}\lbrack i\rbrack} = {{\gamma{\underset{xx}{\hat{\sum}}\left\lbrack {i - 1} \right\rbrack}} + {\frac{1 - \gamma}{K}{{{\hat{R}}_{xx}\lbrack i\rbrack}.}}}} & (21)\end{matrix}$

Finally, the interference covariance matrix is formed in step 6.8 bysubtracting user 0's covariance matrix from the overall covariancematrix.{circumflex over (Σ)}₁ ₀ [i]={circumflex over (Σ)} _(xx) [i]−{circumflexover (Σ)} ₀ [i].  (22)

FIG. 7, which describes subset selection for the non-SDMAimplementation, is very similar to FIG. 3 but without steps 3.8 and 3.9since only one iteration is needed for user m=0.

The short-term loop for the non-SDMA case is like that illustrated inFIG. 4 for the SDMA implementation, but without steps 4 4, 4.9 and 4.10(no iteration over m and with m=0 throughout).

For the non-SDMA embodiment, where the matrix switch is positioned atthe RF level, and the number of RF front-ends is equal to the size ofthe subset, the effective training period must be made longer than in anotherwise similar SDMA embodiment.

Indeed the short-term covariance matrices R₀, R_(xx) must be estimatedin this case in a piecewise fashion by periodically changing the RFswitch to a new subset in such a manner as to process in sequence allpairs of array elements. Hence, the training period must be made longerby a factor of $\frac{\begin{pmatrix}N \\2\end{pmatrix}}{\begin{pmatrix}S \\2\end{pmatrix}}$either by lengthening the training prefix or utilizing severalconsecutive prefixes (in several consecutive packets) in constructing asingle estimate.Space-time Implementation

The implementations described so far were concerned with flat fading(narrowband) channels and therefore only spatial filtering was required.For frequency-selective fading (i.e., wideband) channels, temporalprocessing in the form of equalization must be included in the structureto maintain adequate performance. Thus, each branch of each of the MMSEprocessors (one per desired user sharing a carrier) will include a fullequalizer instead of a single weight. If the subset size is 3, therewould be 3 such equalizers per desired user. An equalizer will typicallytake the form of a tapped-delay line, where each tap is weighted andsummed and taps are symbol-spaced. It follows that an MMSE processorwith 3 branches must then adapt 3L taps, where the equalizer length Lmust be larger than the impulse response of the channel for adequateperformance.

The process of subset selection must also be modified somewhat in afrequency-selective context. Since covariance matrices are in this casefrequency-selective, the original theoretical subset selection criterion(see (10)) can easily be adapted to a wideband operation by integratingover the band (see 23) as follows; $\begin{matrix}{{S_{opt}^{(0)} = {\max\limits_{S_{g}}{\frac{1}{f_{\max} - f_{\min}}{\int_{f_{\min}}^{f_{\max}}{{{tr}\left\lbrack {\underset{0}{\sum\limits^{(S_{g})}}{(f){\underset{I_{0}}{\sum\limits^{(S_{g})}}(f)^{- 1}}}} \right\rbrack}{\mathbb{d}f}}}}}},{{{for}\quad s} = 1},\ldots\quad,{N_{S}\quad{where}}} & (23) \\{{{\underset{0}{\sum\limits^{(S_{g})}}(f)} = {\sum\limits_{k = {- \infty}}^{\infty}\left\langle {{C_{0}^{(S_{g})}\left( {f - \frac{k}{T}} \right)}{c_{0}^{{(S_{g})}^{H}}\left( {F - \frac{k}{T}} \right)}} \right\rangle}},} & (24) \\{{{\underset{I_{0}}{\sum\limits^{(S_{g})}}(f)} = {\sum\limits_{k = {- \infty}}^{\infty}{\sum\limits_{m = 1}^{M}\left\langle {C_{m}^{(S_{g})}\left( {f - \frac{k}{T}} \right){c_{m}^{{(S_{g})}^{H}}\left( {F - \frac{k}{T}} \right)}} \right\rangle}}},} & (25)\end{matrix}$

It should be noted that, in the above, the summation over k reflects thespectral replication associated with symbol-spaced sampling of thesignals, i.e., the covariance matrices have been derived with sampledversions of the channel impulse responses.

The criterion described by (23) can be converted to the time domain byvirtue of the general form of Parseval's relation to yield$\begin{matrix}{{S_{opt}^{(0)} = {{\max\limits_{S_{g}}{\sum\limits_{t = {- \infty}}^{\infty}{{{tr}\left\lbrack {\overset{(S_{g})}{\sum\limits_{0}}{\lbrack i\rbrack{\underset{I_{0}}{\sum\limits^{(S_{g})}}\lbrack i\rbrack^{- 1}}}} \right\rbrack}\quad{for}\quad s}}} = 1}},\ldots\quad,N_{g},{where}} & (26) \\{{{\underset{0}{\sum\limits^{(S_{g})}}\lbrack i\rbrack} = {{{??}^{- 1}\left\lbrack {\underset{0}{\sum\limits^{(S_{g})}}\lbrack f\rbrack} \right\rbrack} = \left\langle {{c_{0}^{(S_{g})}\lbrack I\rbrack}{c_{0}^{{(S_{g})}^{H}}\left\lbrack {l - i} \right\rbrack}} \right\rangle}},} & (27) \\{{{\underset{0}{\sum\limits^{(S_{g})}}\lbrack i\rbrack} = {{{??}^{- 1}\left\lbrack {\underset{0}{\sum\limits^{(S_{g})}}\lbrack f\rbrack} \right\rbrack} = {\sum\limits_{m = 1}^{M}\quad\left\langle {{c_{m}^{(S_{g})}\lbrack I\rbrack}{c_{m}^{{(S_{g})}^{H}}\left\lbrack {l - i} \right\rbrack}} \right\rangle}}},} & (28)\end{matrix}$where ℑ⁻¹[·] denotes the inverse Fourier transform.

In a practical implementation, the ideal covariance matrices would bereplaced by estimates obtained typically via methods similar to thosedescribed in narrowband implementations. Likewise, the summation i in(26) would need to be truncated to the length L of the equalizers.Hence: $\begin{matrix}{{S_{opt}^{(0)} = {{\max\limits_{S_{g}}{\sum\limits_{i = 0}^{L - 1}{{{tr}\left\lbrack {\underset{0}{\sum\limits^{(S_{g})}}{\lbrack i\rbrack{\overset{(S_{g})}{\sum\limits_{I_{0}}}\lbrack i\rbrack^{- 1}}}} \right\rbrack}\quad{for}\quad s}}} = 1}},\ldots\quad,{N_{s}.}} & (29)\end{matrix}$

FIG. 8 shows the general structure of a space-time receiverimplementation with associated signal processing functions.

It should be appreciated that the present invention could be used toadvantage in CDMA in certain situations, for example where some usersconstitute strong interferers. Indeed, it is well-known that one of themajor problems limiting the number of users in CDMA is the presence ofrelatively strong interferers which cannot be eliminated by despreading.This is known as the “near-far effect” and it creates a situationanalogous to SDMA since there are interferers “leaking into” oreffectively coexisting on the virtual carrier corresponding to thedesired user's code. The spatial discrimination power of an adaptivearray combined with the present invention (with modificationsappropriate to the CDMA context) provide a relatively inexpensive andeffective solution.

The present invention is distinguished from known selection diversityarray antenna systems which select the antenna element which gives thebest performance for a particular desired user, since embodiments of thepresent invention select, on an ongoing basis, the subset of antennaelements which give the best global quality index for a particulardesired user.

Preferred embodiments of the invention are predicated upon the factthat:

1. At a base station, most of the energy arriving from a given signalsource is typically concentrated within a narrow angle or cone.Occasionally, there will also be one or more directions of arrival(DOAs) with significant power, but these are typically characterized bya much narrower angle spread than the main DOA. In this context, the useof a radial array of directive elements (or an array of omnidirectionalelements and a preprocessing beamforming matrix which simulates saidradial array through pattern synthesis) implies that a small subset ofelements can be sufficient to capture most of the energy of any oneuser's signal. Using only a subset of the antenna element signalsreduces the processing requirements.2. The medium-term covariance matrix (averaged over the small-scalemultipath fading, i.e. the short term variations in the channelcharacteristics (gains, delays and phases)) of a given user's signalmeasured at the array input varies relatively slowly and can generallybe assumed fixed for periods of the order of a second.

The proposed invention, however, does not seek to identify all degreesof freedom of the desired users channel, but rather to select(exploiting the directivity of the array elements when a radial array isused) the S most significant elements in order to achieve the minimummean-square error. Such a selection is not really based on identifyingthe degrees-of-freedom, or modes, of the desired users channel sinceinterferers are also taken into account in the selection process. It isa procedure to intelligently reduce (by exploiting the geometry of theimpinging waves) the number of array degrees-of-freedom that requireactive adaptation in order to achieve a proportional reduction in bothnumerical and hardware complexity.

Although ten, input ports and ten antenna elements are shown, thatnumber is chosen only for purposes of illustration. In practice, theremight be even more depending on practical considerations such as cost,physical array size, etc.). Likewise, although FIG. 1 shows a subset of3, the most useful choices (depending on the desiredcomplexity/performance tradeoff) are likely to be between 2 and 4elements, inclusively. Furthermore, it should be noted that the relativecomplexity reduction introduced by this invention with respect tostandard MMSE array processing is approximately proportional to N/S.

The receiver system illustrated in FIG. 5 has the advantage ofnecessitating only as many RF front-end units as the number of elementsin a subset (3 in the specific example). Typically, an RF front-end isboth bulky and relatively expensive and it is therefore advantageous toreduce the number of such units with respect to a fully adaptive array.However, the RF matrix switch 16 also can be an expensive component andmay in some cases (depending on the carrier frequency and bandwidth)nullify the cost advantage stemming from the reduced number of RFfront-ends. In the receiver system illustrated in FIG. 1, where allarray elements are each equipped with a signal receiving unit(front-end) and the matrix switch is placed after A/D conversion, thesaid switch is then no longer an expensive RF component but rather adigital multiplexer capable of multiplexing 6 serial or parallel datastreams onto 3. Alternatively, the multiplexer can be absorbed into thesignal processing unit 14 provided the latter has sufficient inputresources. Conversely, the subset selection logic could be separated.

While it is general practice to assume that the channels can beconsidered static over the length of a block (i.e., the length of ablock is significantly smaller than the channel correlation time), thepresent invention is applicable equally well in other cases wherecontinuous tracking (using adaptive algorithms such as theleast-mean-square (LMS) or the recursive-least-squares (RLS) algorithm)is necessary,

If in fact continuous tracking is implemented, it may not be necessaryto provide frequent training sequences. Indeed, both subset selectionand weight computation updates can be performed using past decisions astraining symbols, provided the latter are reliable. Hence, trainingsequences, while less frequent, would still be required to: (1)initialize the system when a new link is formed so that its firstdecisions are reliable enough to start the tracking procedure; and (2)periodically reset the system to minimize errors due to lost tracking.

It should be appreciated that the invention is not limited to use inbase stations of cellular telephone systems, but could also be used inmobile stations of such systems. Moreover, receivers according to theinvention could be used in, for example, wireless local area networks,packet radio networks, and other wireless networks.

It should be noted that the invention embraces not only the arrayreceiver systems described hereinbefore but also the receiver per se foruse with an array of antenna elements and the signal processor forretrofitting to an exiting array antenna receiver system.

To recapitulate, the adaptation algorithm comprises two loops. Thelong-term loop in the narrowband case can be broken down as follows:

A. For every user m, m=0, . . . , M+1:

-   -   The short-term covariance matrix of user m's signature over all        N antenna elements is estimated based on a known training        sequence transmitted by user    -   The short-term estimate is used to update a running estimate of        the medium-term-averaged covariance matrix of user m's signature        (6).    -   Using the medium-term averaged covariance matrices computed for        all users, compute the covariance matrix of the interference        seen by user m: $\begin{matrix}        {\sum\limits_{I_{m}}{= {\underset{\begin{matrix}        {i = 0} \\        {i \neq m}        \end{matrix}}{\sum\limits^{M}}{\sum\limits_{i}.}}}} & (30)        \end{matrix}$        B. For all subsets {S_(s)}_(s = 0)^(N_(s)):    -   Select appropriate elements in Σ_(m) and Σ_(l) _(m) to form        Σ_(m) ^((S) ^(s) ⁾ and Σ_(l) _(m) ^((S) ^(s) ⁾.    -   Compute subset selection criterion as per (4).    -   Compare with previously computed maximum value of criterion        (compare with zero if first iteration).    -   If new value is larger save it and corresponding subset index.    -   Repeat loop B until all N_(s) subsets have been processed.    -   Transfer selected subset index S_(m) to subset selector for user        m.    -   Repeat from A until all users have been processed.    -   Wait for next long-term training interval and repeat loop A.        The short-term loop proceeds as follows:        C. For every user m, m=0, . . . , M:    -   Estimate S×S short-term covariance matrix R_(yy) ^((S) ^(m) ⁾        across subset S_(m). This may be done independently for each        user according to (9) or the overall N×N short-term covariance        matrix can be computed once and used to produce (by selecting        the appropriate elements) the required S×S covariance matrices        across all users' respective subsets.    -   Estimate user m's spatial signature c_(m) ^((S) ^(m) ⁾ across        subset S_(m) using (8).    -   Compute the weight vector w=R_(yy) ^((S) ^(m) ⁾⁻¹c_(i) ^((S)        ^(m) ⁾.    -   Transfer the weights to MMSE processor m.    -   Repeat from C for all users.    -   Wait for next short-term training interval (next packet from        same user group) and repeat loop C.

INDUSTRIAL APPLICABILITY

It is known that antenna arrays with appropriate signal processingmeans, when employed in wireless networks, allow more links to coexistsimultaneously in the same band /carrier and/or provide better linkquality (in terms of voice quality in telephony, bit error rate in datalinks, or robustness against fading).

As wireless systems evolve, three factors emerge as being of paramountimportance:

-   -   (i) the switch from analog to digital;    -   (ii) the increasing predominance of broadband channels (which        often require ISI mitigation) to accommodate large data rates;    -   (iii) the capacity bottleneck from which many cellular systems        suffer.

The implementation of a space-time receiver at the base station incombination with SDMA is without a doubt the most promising avenue forincreasing capacity in broadband wireless systems. Indeed, an N-elementarray can theoretically bring an N-fold increase in capacity (i.e.number of simultaneously active users per carrier). However, the cost ofdeveloping and implementing such devices is significant since eachadditional antenna element requires an additional front-end receiver andadditional computing power in order to adapt taps (weights) and performother signal processing tasks.

Therefore the complexity (and hence the cost) of introducing aconventional array system into an existing wireless network can beprohibitive.

The widespread acceptance of antenna arrays and space-time processors inthe marketplace is only a matter of time and recent industry interestconfirms this. Reluctance in the past has probably been due to therelative complexity/cost of these solutions. Although advances intechnology (which lead to lower device costs) and the urgency of thecapacity problem may have overcome some hesitations, complexity is stilla very real issue especially at high bandwidths and/or at highfrequencies.

The present invention provides a less complex solution. In fact, it canprovide a reduction in complexity of an order of magnitude with respectto a canonical linear space-time receiver with minimal performancedegradation.

It should be noted that, when compared with other subset selection arraysystems, the present invention provides better performance by selectingsubsets based on subset performance, not individual branches.Furthermore, the subset selection criterion takes into accountinterference and interference correlation across the array.

To limit the overhead of evaluating and selecting subsets, the presentinvention also proposes a method of subset selection based upon longterm statistics (with respect to the fading rate), which can, in certainembodiments, reduce the complexity of the hardware and/or softwareinvolved in subset selection by an order of magnitude.

The proposed invention differs in its applicability; indeed, its purposeis to mitigate co-channel interference as well as provide robustnessagainst fading while the two selection diversity schemes mentioned aregenerally studied for robustness against fading alone. Furthermore, theproposed invention exploits the geometry of arriving signals at the basestation through the use of radially-arranged directional elements. Theselection of subsets based on medium-term statistics is also a novelconcept.

It should be noted that the benefits of this invention do not requireSDMA or wideband (i.e. space-time) operation. This makes it anattractive path for incremental upgrade of existing systems.

1. An array receiver system, for receiving signals from a plurality oftransmitting users, comprising an array of antenna elements (22/1, . . ., 22/10) and a receiver having a plurality of receiver sections (12 ₀, .. . , 12 ₇), each corresponding to a different one of the users, thereceiver sections each having a signal processing unit (16 ₀) forprocessing and combining a subset of signals from the antenna elementsto produce a received signal for the corresponding user, the receiverfurther comprising switching means (18 ₀) for selecting a plurality ofdifferent subsets of signals from the antenna elements for processingfor the signal processing unit (16 ₀), each subset consisting of apredetermined number of said signals, each signal processing meansserving to control the switching means to change the signals comprisingthe subset of signals used by the corresponding receiver section independence upon a measure of the potential performance of that receiversection with different subsets of said plurality of signals, saidmeasure being based upon the combined subset of signals.
 2. An arrayantenna radio receiver system according to claim 1, wherein theswitching means comprises a switch matrix in each receiver section, andthe receiver comprises a plurality of radio frequency (RF) front-endsections each coupling a respective one of the antenna elements to eachof said switching means and each of the signal processing means, eachfront-end section for converting the signal from the correspondingantenna element to a format suitable for processing by said processingmeans, and each of said switch matrices selects subsets of the convertedsignals for application to the associated one of the different receiversections.
 3. An array antenna receiver system according to claim 2,wherein the processing unit: periodically selects samples of the signalsfrom the antenna elements; uses the signal samples to compute acovariance matrix for each of the users; uses the covariance matrices ofall users to compute, for the associated user; an interferencecovariance matrix characterizing the sum of the interfering signals ofothers of said users; selects each possible subset of the covariancematrices and the interference covariance matrices having the sameprescribed number as elements in the subset; for each selected subset ofsignals and associated covariance matrices, computes said performancecriterion; and for its own user, selects the subset that gives the bestperformance criterion.
 4. An array receiver system according to claim 3,wherein the signal processing unit computes, as said measure, SINR asthe trace of the covariance matrix estimate for the particular user mand subset times the inverse of the interference covariance matrixestimate for the particular user and subset selection according to theexpression$C = {{{tr}\quad\left\lbrack {\sum\limits_{m}^{\overset{\bigwedge}{(S_{s})}}\quad\left( \sum\limits_{I_{m}}^{\overset{\bigwedge}{(S_{s})}} \right)^{- 1}} \right\rbrack}.}$5. A receiver system according to claim 3, wherein the signal processingunit is arranged to monitor channel parameters for a particular subsetselection and, in dependence upon said parameters, update eachcovariance matrix, said update occurring more frequently than subsetselection.
 6. A receiver system according to claim 3, wherein thesignals received by the antenna elements comprise packets havingembedded training sequences and, at preset estimation intervals, eachprocessing means selects one of the different subsets of signals,samples said packets, extracts the training sequence, and uses thetraining sequence to obtain said measure of performance for theparticular subset selected.
 7. An array antenna radio receiver systemaccording to claim 1, wherein each receiver section comprises aplurality of radio frequency (RF) front-end units equal in number to thenumber of signals in each of said subsets coupled to the signalprocessing means, and the switching means comprises a switch matrix forcoupling selected ones of the antenna elements to respective ones of theRF front-end sections of each receiver section, each RF front-endsection for converting the subset of signals from the correspondingantenna elements to a format suitable for processing by said processingmeans.
 8. An array antenna receiver system according to claim 1, whereineach signal processing means measures said potential performance of thecorresponding receiver section with all different possible subsets ofsaid plurality of signals.
 9. An array antenna receiver system accordingto claim 1, wherein each signal processing unit measures saidperformance by monitoring statistics of the signals derived from thedifferent subsets over a time period long enough to average out fastfading effects due to phase relationships of multipath components of thesubset signals.
 10. A receiver system according to claim 1, wherein eachsignal processing unit is arranged to use minimum mean squared error(MMSE) in adaptively weighting and combining each subset of signals,determine a second performance criterion of each subset over a shortertime period than the first-mentioned time period, and adjust weightsused by the MMSE process in dependence upon such short time periodmeasurement.
 11. A receiver system according to claim 10, wherein eachsignal processing unit determines said second performance criterion onthe basis of signals from the current subset of antenna elements.
 12. Areceiver system according to claim 1, wherein the antenna elements arearranged in a radial array of directive elements.
 13. A receiver systemaccording to claim 12, wherein the antenna elements are configured suchthat sectors corresponding to radiation/sensitivity lobes of theadjacent antenna elements partially overlap.
 14. A receiver systemaccording to claim 13, wherein the processor uses Minimum Mean SquareError (MMSE) processing in combining and processing the subset ofsignals, and the processing means uses the channel parameters to updateweights used in said MMSE processing.
 15. A method of receiving signalsfrom a plurality of transmitting users using an array antenna having anarray of antenna elements (22/1, . . . , 22/10) and a receiver having aplurality of receiver sections, each corresponding to a different one ofthe users, and coupled to the antenna elements by a switching means, themethod comprising the steps of; periodically selecting different subsetsof signals from the antenna elements, processing and combining eachsubset of signals and determining potential performance of the receiversection of a particular user with that subset, determining which of thesubsets would provide best performance, and controlling the switchingmeans to change the signals comprising the subset of signals used by thecorresponding receiver section.
 16. A method according to claim 15,wherein the signals from the antenna elements are each converted to aform suitable for processing by the signal processing unit and theselection of the subset is made by selecting the converted signals. 17.A method according to claim 15, wherein the receiver comprises a singlesection having a plurality of RF front end units equal to the number ofsignals in the subset, and the selection of the subsets is made byselecting a subset of the signals from the antenna elements and applyingthe subset to the RF front end units.
 18. A method according to claim15, wherein the measure of said potential performance is made for alldifferent possible subsets of said plurality of signals.
 19. A methodaccording to claim 15, wherein the performance is measured by monitoringstatistics of the signals derived from the different subsets over a timeperiod long enough to average out fast fading effects due to phaserelationships of multipath components of the subset signals.
 20. Amethod according to claim 19, comprising the steps of: periodicallyselecting samples of said subset of the signals from the antennaelements, using the signal samples to compute a covariance matrix foreach of the users, using the covariance matrices of all users tocompute, for the associated user, an interference covariance matrixcharacterizing the sum of the interfering signals of others of saidusers selecting each possible subset of the covariance matrices and theinterference covariance matrices having the same prescribed number aselements in the subset, for each selected subset of matrices, computingsaid performance criterion; and for the particular user, selecting thesubset that gives the best performance criterion.
 21. A method accordingto claim 20, wherein said measure is SINR computed as the trace of thecovariance matrix estimate for the particular user and subset times theinverse of the interference covariance matrix estimate for theparticular user and subset selection according to the expression$\begin{matrix}{C = {{{tr}\quad\left\lbrack {\sum\limits_{m}^{\overset{\bigwedge}{(S_{s})}}\quad\left( \sum\limits_{I_{m}}^{\overset{\bigwedge}{(S_{s})}} \right)^{- 1}} \right\rbrack}.}} & (24)\end{matrix}$
 22. A method according to claim 20, wherein channelparameters are monitored for a particular subset selection and, independence upon said parameters, each covariance matrix updated morefrequently than subset selection.
 23. A method according to claim 20,wherein the signals received by the antenna elements comprise packetshaving embedded training sequences and, at preset estimation intervals,one of the different subsets of signals is selected, said packetssampled, the training sequence extracted, and the training sequence usedto obtain said measure of performance for the particular subsetselected.
 24. A method according to claim 15, wherein the subset ofsignals are processed using minimum mean squared error (MMSE) toadaptively weight and combine each subset of signals, and a secondperformance criterion of each subset is measured over a shorter timeperiod than the first-mentioned time period, and weights used by theMMSE process adjusted in dependence upon such short time periodmeasurement.
 25. A method according to claim 24, wherein said secondperformance criterion is determined on the basis of signals from thecurrently-selected subset of antenna elements.
 26. A method according toclaim 24, wherein the MMSE uses the channel parameters to update theweights.
 27. A receiver for use with an array antenna having a pluralityof antenna elements to receive signals from a plurality of transmittingusers, the receiver having a plurality of receiver sections (12 ₀, . . ., 12 ₇), each corresponding to a different one of the users, thereceiver sections each having a signal processing unit (16 ₀) forprocessing and combining a subset of signals from the antenna elementsto produce a received signal for the corresponding user, the receiverfurther comprising switching means (18 ₀) for selecting a plurality ofdifferent subsets of signals from the antenna elements for processingfor the signal processing unit (16 ₀), each subset consisting of apredetermined number of said signals, each signal processing meansserving to control the switching means to change the signals comprisingthe subset of signals used by the corresponding receiver section independence upon a measure of the potential performance of that receiversection with different subsets of said plurality of signals, saidmeasure being based upon the combined subset of signals.